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Industrial insertion tasks are often performed repetitively with parts that are subject to tight tolerances and prone to breakage. Learning an industrial insertion policy in real is challenging as the collision between the parts and the environment c
Externí odkaz:
http://arxiv.org/abs/2210.01340
Folding garments reliably and efficiently is a long standing challenge in robotic manipulation due to the complex dynamics and high dimensional configuration space of garments. An intuitive approach is to initially manipulate the garment to a canonic
Externí odkaz:
http://arxiv.org/abs/2208.10552
Robot learning of real-world manipulation tasks remains challenging and time consuming, even though actions are often simplified by single-step manipulation primitives. In order to compensate the removed time dependency, we additionally learn an imag
Externí odkaz:
http://arxiv.org/abs/2107.02464
Autor:
Berscheid, Lars, Kröger, Torsten
We present Ruckig, an algorithm for Online Trajectory Generation (OTG) respecting third-order constraints and complete kinematic target states. Given any initial state of a system with multiple Degrees of Freedom (DoFs), Ruckig calculates a time-opti
Externí odkaz:
http://arxiv.org/abs/2105.04830
Robot learning is often simplified to planar manipulation due to its data consumption. Then, a common approach is to use a fully-convolutional neural network to estimate the reward of grasp primitives. In this work, we extend this approach by paramet
Externí odkaz:
http://arxiv.org/abs/2103.12810
Despite its utopian promises as a disruptive equalizer, AI - like most tools deployed under the guise of neutrality - has tended to simply reinforce existing social structures. To counter this trend, radical AI calls for centering on the marginalized
Externí odkaz:
http://arxiv.org/abs/2012.01128
Flexible pick-and-place is a fundamental yet challenging task within robotics, in particular due to the need of an object model for a simple target pose definition. In this work, the robot instead learns to pick-and-place objects using planar manipul
Externí odkaz:
http://arxiv.org/abs/2006.08373
Autor:
Emslie, Ethan, Mui, Justin, Sullivan, M. Brad, Karreman, Erwin, Buitenhuis, Deanna, Berscheid, Kasey, Rattray, Darrien
Publikováno v:
In The Journal of Minimally Invasive Gynecology November 2023 30(11):905-911
Akademický článek
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Robotic grasping in cluttered environments is often infeasible due to obstacles preventing possible grasps. Then, pre-grasping manipulation like shifting or pushing an object becomes necessary. We developed an algorithm that can learn, in addition to
Externí odkaz:
http://arxiv.org/abs/1907.11035